Literature DB >> 19029492

Assessing new biomarkers and predictive models for use in clinical practice: a clinician's guide.

Kevin McGeechan1, Petra Macaskill, Les Irwig, Gerald Liew, Tien Y Wong.   

Abstract

New biomarkers and predictive models that aim to improve the identification of people at risk of cardiovascular disease are constantly proposed. Clinicians need to be aware of the various methods used to assess these biomarkers and models and how these should be interpreted. New biomarkers and models are assessed in terms of their contribution to global fit, discrimination, calibration, and reclassification. These measures, when used in isolation, do not address the clinically important questions of whether the new model predicts risk more accurately than existing models and whether the risks predicted for individuals are sufficiently different to warrant a change in treatment decisions. We recommend that these measures be supplemented with graphical displays such as a calibration plot for the Hosmer-Lemeshow test and a scatterplot of the risks predicted by the models being compared. We encourage researchers to report such analyses from studies on the clinical utility of new biomarkers because this information is pertinent for the clinician who must decide whether to test for a new biomarker in their clinical practice.

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Year:  2008        PMID: 19029492     DOI: 10.1001/archinte.168.21.2304

Source DB:  PubMed          Journal:  Arch Intern Med        ISSN: 0003-9926


  53 in total

1.  The PREMM(1,2,6) model predicts risk of MLH1, MSH2, and MSH6 germline mutations based on cancer history.

Authors:  Fay Kastrinos; Ewout W Steyerberg; Rowena Mercado; Judith Balmaña; Spring Holter; Steven Gallinger; Kimberly D Siegmund; James M Church; Mark A Jenkins; Noralane M Lindor; Stephen N Thibodeau; Lynn Anne Burbidge; Richard J Wenstrup; Sapna Syngal
Journal:  Gastroenterology       Date:  2010-08-19       Impact factor: 22.682

2.  Assessment of multiple cardiac biomarkers in non-ST-segment elevation acute coronary syndromes: observations from the MERLIN-TIMI 36 trial.

Authors:  Benjamin M Scirica; Marc S Sabatine; Petr Jarolim; Sabina A Murphy; James L de Lemos; Eugene Braunwald; David A Morrow
Journal:  Eur Heart J       Date:  2010-12-22       Impact factor: 29.983

Review 3.  Analysis of biomarker data: logs, odds ratios, and receiver operating characteristic curves.

Authors:  Birgit Grund; Caroline Sabin
Journal:  Curr Opin HIV AIDS       Date:  2010-11       Impact factor: 4.283

4.  Commentary: C-reactive protein and risk prediction--moving beyond associations to assessing predictive utility and clinical usefulness.

Authors:  Ramachandran S Vasan
Journal:  Int J Epidemiol       Date:  2009-01-07       Impact factor: 7.196

5.  A comparison of prediction models for fractures in older women: is more better?

Authors:  Kristine E Ensrud; Li-Yung Lui; Brent C Taylor; John T Schousboe; Meghan G Donaldson; Howard A Fink; Jane A Cauley; Teresa A Hillier; Warren S Browner; Steven R Cummings
Journal:  Arch Intern Med       Date:  2009-12-14

6.  Assessing the performance of prediction models: a framework for traditional and novel measures.

Authors:  Ewout W Steyerberg; Andrew J Vickers; Nancy R Cook; Thomas Gerds; Mithat Gonen; Nancy Obuchowski; Michael J Pencina; Michael W Kattan
Journal:  Epidemiology       Date:  2010-01       Impact factor: 4.822

Review 7.  Personalized cardiovascular medicine: concepts and methodological considerations.

Authors:  Henry Völzke; Carsten O Schmidt; Sebastian E Baumeister; Till Ittermann; Glenn Fung; Janina Krafczyk-Korth; Wolfgang Hoffmann; Matthias Schwab; Henriette E Meyer zu Schwabedissen; Marcus Dörr; Stephan B Felix; Wolfgang Lieb; Heyo K Kroemer
Journal:  Nat Rev Cardiol       Date:  2013-03-26       Impact factor: 32.419

8.  Derivation and validation of a new cardiovascular risk score for people with type 2 diabetes: the new zealand diabetes cohort study.

Authors:  C Raina Elley; Elizabeth Robinson; Tim Kenealy; Dale Bramley; Paul L Drury
Journal:  Diabetes Care       Date:  2010-03-18       Impact factor: 17.152

9.  An independent and external validation of QRISK2 cardiovascular disease risk score: a prospective open cohort study.

Authors:  Gary S Collins; Douglas G Altman
Journal:  BMJ       Date:  2010-05-13

Review 10.  Reporting performance of prognostic models in cancer: a review.

Authors:  Susan Mallett; Patrick Royston; Rachel Waters; Susan Dutton; Douglas G Altman
Journal:  BMC Med       Date:  2010-03-30       Impact factor: 8.775

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